In this paper we present a hybrid approach to integrative clustering based on the p-median problem with clients’ preferences. We formulate the problem of simultaneous clustering of a set of objects, characterized by two sets of features, as a bi-level p-median model. An exact approach involving a branchand-cut method combined with the simulated annealing algorithm is used, that allows one to fi...

The recent years have witnessed a growing interest in two advanced strategies to cope with the data clustering problem, namely, clustering ensembles and multi-objective clustering. In this paper, we present a genetic programming based approach that can be considered as a hybrid of these strategies, thereby allowing that different hierarchical clustering ensembles be simultaneously evolved takin...

Clustering is a widely studied problem in data mining. Ai techniques, evolutionary techniques and optimization techniques are applied to this field. In this study, a novel hybrid modeling approach proposed for clustering and feature selection. Ant colony clustering technique is used to segment breast cancer data set. To remove irrelevant or redundant features from data set for clustering Sequen...

Prolonged network lifetime, scalability, and load balancing are important requirements for many adhoc sensor network applications. Clustering sensor nodes is an effective technique for achieving these goals. In this work, we propose a new hybrid energy-efficient approach for clustering nodes in ad-hoc sensor networks. Based on this approach, we present a protocol, HEED (Hybrid Energy-Efficient ...

This paper further extends the ‘kernel’-based approach to clustering proposed by E. Diday in early 70s. According to this approach, a cluster’s centroid can be represented by parameters of any analytical model, such as linear regression equation, built over the cluster. We address the problem of producing regressionwise clusters to be separated in the input variable space by building a hybrid c...

In this paper we present top-down and bottom-up hierarchical clustering methods for large bipartite graphs. The top down approach employs a flow-based graph partitioning method, while the bottom up approach is a multiround hybrid of the single-link and average-link agglomerative clustering methods. We evaluate the quality of clusters obtained by these two methods using additional textual inform...

This paper introduces a new hybrid algorithmic nature inspired approach based on the concepts of the Honey Bees Mating Optimization Algorithm (HBMO) and of the Greedy Randomized Adaptive Search Procedure (GRASP), for optimally clustering N objects into K clusters. The proposed algorithm for the Clustering Analysis, the Hybrid HBMO-GRASP, is a two phase algorithm which combines a HBMO algorithm ...

This paper introduces a hybrid approach that is based on color information that utilizes background subtraction technique, a mask and K-Mean clustering algorithm. This hybrid approach efficiently removes artifacts caused by lightening changes such as highlight, reflection, and shadows of moving objects from segmentation. We first create a mask by assigning values to R, G and B channels utilizin...